An implicit Markov random field model for the multi-scale oriented representations of natural images

Siwei Lyu

doi:10.1109/CVPRW.2009.5206797

An implicit Markov random field model for the multi-scale oriented representations of natural images

Siwei Lyu

Department of Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

In this paper, we describe a new Markov random field (MRF) model for natural images in multi-scale oriented representations. The MRF in this model is specified with the singleton conditional densities (the density of one subband coefficient given its Markovian neighbors), while the clique potentials and joint density of this model are implicitly defined. The singleton conditional densities are chosen to have maximum entropy and consistent with observed statistical properties of natural images. We then describe parameter learning for this model, and a sparse prior to choose optimal model structure. Using this model as image prior, we develop an iterative image denoising method, and a solution to restoring images with missing blocks of subband coefficients.

Original language	English
Title of host publication	2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009
Publisher	IEEE Computer Society
Pages	1919-1925
Number of pages	7
ISBN (Print)	9781424439935
DOIs	https://doi.org/10.1109/CVPRW.2009.5206797
State	Published - 2009
Event	2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009 - Miami, FL, United States Duration: Jun 20 2009 → Jun 25 2009

Publication series

Name	2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009

Conference

Conference	2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009
Country/Territory	United States
City	Miami, FL
Period	06/20/09 → 06/25/09

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10.1109/CVPRW.2009.5206797

Cite this

Lyu, S. (2009). An implicit Markov random field model for the multi-scale oriented representations of natural images. In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009 (pp. 1919-1925). Article 5206797 (2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009). IEEE Computer Society. https://doi.org/10.1109/CVPRW.2009.5206797

@inproceedings{b3560af194524e07bc10f276485afdd8,

title = "An implicit Markov random field model for the multi-scale oriented representations of natural images",

abstract = "In this paper, we describe a new Markov random field (MRF) model for natural images in multi-scale oriented representations. The MRF in this model is specified with the singleton conditional densities (the density of one subband coefficient given its Markovian neighbors), while the clique potentials and joint density of this model are implicitly defined. The singleton conditional densities are chosen to have maximum entropy and consistent with observed statistical properties of natural images. We then describe parameter learning for this model, and a sparse prior to choose optimal model structure. Using this model as image prior, we develop an iterative image denoising method, and a solution to restoring images with missing blocks of subband coefficients.",

author = "Siwei Lyu",

year = "2009",

doi = "10.1109/CVPRW.2009.5206797",

language = "English",

isbn = "9781424439935",

series = "2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009",

publisher = "IEEE Computer Society",

pages = "1919--1925",

booktitle = "2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009",

address = "United States",

note = "2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009 ; Conference date: 20-06-2009 Through 25-06-2009",

}

Lyu, S 2009, An implicit Markov random field model for the multi-scale oriented representations of natural images. in 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009., 5206797, 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, IEEE Computer Society, pp. 1919-1925, 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, Miami, FL, United States, 06/20/09. https://doi.org/10.1109/CVPRW.2009.5206797

An implicit Markov random field model for the multi-scale oriented representations of natural images. / Lyu, Siwei.
2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. IEEE Computer Society, 2009. p. 1919-1925 5206797 (2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - In this paper, we describe a new Markov random field (MRF) model for natural images in multi-scale oriented representations. The MRF in this model is specified with the singleton conditional densities (the density of one subband coefficient given its Markovian neighbors), while the clique potentials and joint density of this model are implicitly defined. The singleton conditional densities are chosen to have maximum entropy and consistent with observed statistical properties of natural images. We then describe parameter learning for this model, and a sparse prior to choose optimal model structure. Using this model as image prior, we develop an iterative image denoising method, and a solution to restoring images with missing blocks of subband coefficients.

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Lyu S. An implicit Markov random field model for the multi-scale oriented representations of natural images. In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. IEEE Computer Society. 2009. p. 1919-1925. 5206797. (2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009). doi: 10.1109/CVPRW.2009.5206797

An implicit Markov random field model for the multi-scale oriented representations of natural images

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